Modifying a computer-based interaction based on eye gaze

ABSTRACT

A method modifies a computer-based interaction based on gaze data. One or more processors collect eye gaze data points to create an eye gaze corpus of information, where the eye gaze data points describe an eye gaze of viewers of a first set of at least one user interface. The processor(s) generate a plurality of clusters of viewers, and determine a target action performance for each of the plurality of clusters. The processor(s) collect, from a device having eye tracking technology, real time eye gaze data from a plurality of current users who are viewing a second set of at least one user interface, and segment the plurality of current users. The processor(s) then modify a computer-based interaction for at least one segment in order to maximize target action performance of the second set of at least one user interface.

BACKGROUND

The present invention relates to the field of computers, andparticularly to computers that receive and/or transmit electronicinformation. Still more particularly, the present invention relates todynamically modifying electronic information based on eye gaze.

Anticipating user interaction is an important element in human-machineinterface design. Guiding a user to perform a targeted action (e.g. makea purchase, download software or follow a call-to-action) is an area offocus across industries.

The success rate of users starting and completing a target digitalaction can greatly depend on user experience (UX) presentation, how wellinformation about the user is leveraged, and external factors leading upthe interaction. A good example of this is online e-commerce websites.The desired target action is a user purchase. The provider of thee-commerce websites makes use of a purchase history of its user base, inaddition to other metadata, in order to suggest additional products thatmay also be of interest to the user, with the goal of maximizing theoverall purchase.

Similar practices have been applied across other enterprises, usinganalytics to segment a user base for delivering targeted content (whichmay be in the form of products or services) to increase the likelihoodof a user performing a target action. Formally defined, usersegmentation is the practice of dividing a user base into groups thatreflect similarity among users in each group. The goal of segmentingusers is to decide how to relate to users in each segment in order tomaximize the value of each user to a business.

For instance, certain processes track user behavior across a number ofdimensions, and apply analytics to determine the likelihood of a usercompleting an action, as well as to infer potential blockers tocompleting the action.

In such products and offerings, the inputs to the system arewell-defined. Legacy metrics (for example: click-through rates, pageviews, view durations, etc.), coupled with any available user anddemographic metadata, are used to facilitate user segmentation.

However, no system or associated method exists for using an unsupervisedmachine learning approach to segment (i.e., cluster) a user basepopulation using eye gaze tracking data to infer optimal (content andformat) for delivery to the user.

The present invention provides a new and useful solution of providingsuch a system and associated methods.

SUMMARY

In one or more embodiments of the present invention, a method modifies acomputer-based interaction based on gaze data. One or more processorscollect eye gaze data points to create an eye gaze corpus ofinformation, where the eye gaze data points describe an eye gaze ofviewers of a first set of at least one user interface. The processor(s)apply an unsupervised machine learning algorithm to the eye gaze corpusto generate a plurality of clusters based upon the eye gaze data points,and determine a target action performance of the first set of at leastone user interface for each of the plurality of clusters. Theprocessor(s) collect, from a device having eye tracking technology, realtime eye gaze data from a plurality of current users who are viewing asecond set of at least one user interface, and segment the plurality ofcurrent users based upon the plurality of clusters by analyzing patternsamong the real-time eye gaze data and by analyzing scenario datapertaining to the target action performance. Responsive to thesegmenting of the plurality of users, the processor(s) modify acomputer-based interaction for at least one segment of the at least oneof the plurality of users in order to maximize target action performanceof the second set of at least one user interface. Such an embodimentprovides a technological-based improvement over the prior art bydynamically modifying a computer-based interaction, thus making thesystem more efficient in generating clarifications to graphical userinterface content, and further improving a user experience.

In one or more embodiments of the present invention, the scenario datapertaining to target action performance includes historical data onfailed or successful scenarios with respect to completion of at leastone target action. Such an embodiment provides a technologicalimprovement over the prior art by dynamically improving thecomputer-based interaction and user experience based on historicalscenarios.

In one or more embodiments of the present invention, modifying thecomputer-based interaction with at least one of the plurality of usersincludes modifying at least one aspect of a graphical user interface ofa software program, such as displaying promotional content via thegraphical user interface, displaying additional clarification contentrelated to the software program via the graphical user interface, and/orpresenting a conversational interface on the graphical user interface.Such an embodiment provides a technological improvement over the priorart by automatically adding the conversational interface, therebyimproving the computer-based interaction and user experience.

In one or more embodiments of the present invention, the eye gaze ofviewers of the first set of at least one user interface and thereal-time eye gaze data from the plurality of current users who areviewing the second set of at least one user interface are based on apathological nystagmus of the viewers of the first set of at least oneuser interface and the plurality of current users who are viewing thesecond set of at least one user interface. Such an embodiment provides atechnological improvement over the prior art by providing additionalaccessibility to low vision persons when participating in acomputer-based interaction.

In one or more embodiments of the present invention, the eye gaze ofviewers of the first set of at least one user interface and thereal-time eye gaze data from the plurality of current users who areviewing the second set of at least one user interface are based on aposition on a graphical user interface at which the viewers of the firstset of at least one user interface and the plurality of current userswho are viewing the second set of at least one user interface arevisually focused. Such an embodiment provides a technologicalimprovement over the prior art by dynamically improving thecomputer-based interaction and user experience based on historicalgazes.

In one or more embodiments of the present invention, a computer programproduct for modifying a computer-based interaction based on historicaleye gaze data includes the computer program product having anon-transitory computer readable storage device, with programinstructions embodied therewith, such that the program instructions arereadable and executable by a computer to perform a method of: collectingeye gaze data points to create an eye gaze corpus of information, wherethe eye gaze data points describe an eye gaze of viewers of a first setof at least one user interface; applying an unsupervised machinelearning algorithm to the eye gaze corpus to generate a plurality ofclusters based upon the eye gaze data points; determining a targetaction performance of the first set of at least one user interface foreach of the plurality of clusters; collecting, from a device having eyetracking technology, real time eye gaze data from a plurality of currentusers who are viewing a second set of at least one user interface;segmenting the plurality of current users based upon the plurality ofclusters by analyzing patterns among the real-time eye gaze data and byanalyzing scenario data pertaining to the target action performance; andresponsive to the segmenting of the plurality of users, modifying acomputer-based interaction for at least one segment of the at least oneof the plurality of users in order to maximize target action performanceof the second set of at least one user interface. Such an embodimentprovides a technological-based improvement over the prior art bydynamically modifying a computer-based interaction, thus making thesystem more efficient in generating clarifications to graphical userinterface content, and further improving a user experience.

In one or more embodiments of the present invention, a computer systemincludes one or more processors, one or more computer readable memories,one or more computer readable storage mediums, and program instructionsstored on at least one of the one or more storage mediums for executionby at least one of the one or more processors via at least one of theone or more memories. The stored program instructions include: programinstructions to collect eye gaze data points to create an eye gazecorpus of information, where the eye gaze data points describe an eyegaze of viewers of a first set of at least one user interface; programinstructions to apply an unsupervised machine learning algorithm to theeye gaze corpus to generate a plurality of clusters based upon the eyegaze data points; program instructions to determine a target actionperformance of the first set of at least one user interface for each ofthe plurality of clusters; program instructions to collect, from adevice having eye tracking technology, real time eye gaze data from aplurality of current users who are viewing a second set of at least oneuser interface; program instructions to segment the plurality of currentusers based upon the plurality of clusters by analyzing patterns amongthe real-time eye gaze data and by analyzing scenario data pertaining tothe target action performance; and program instructions to, responsiveto segmenting the plurality of users, modify a computer-basedinteraction for at least one segment of the at least one of theplurality of users in order to maximize target action performance of thesecond set of at least one user interface. Such an embodiment provides atechnological-based improvement over the prior art by dynamicallymodifying a computer-based interaction, thus making the system moreefficient in generating clarifications to graphical user interfacecontent, and further improving a user experience.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary system and network in which the presentdisclosure may be implemented;

FIG. 2 illustrates a high-level overview of one or more embodiments ofthe present invention;

FIG. 3 depicts an exemplary computer-based interaction that has beenmodified in accordance with one or more embodiments of the presentinvention;

FIG. 4 is a high-level flow chart of one or more steps performed by oneor more processors and/or other hardware devices in accordance with oneor more embodiments of the present invention;

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 6 depicts abstraction model layers of a cloud computer environmentaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Hash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

With reference now to the figures, and in particular to FIG. 1, there isdepicted a block diagram of an exemplary system and network that may beutilized by and/or in the implementation of the present invention. Someor all of the exemplary architecture, including both depicted hardwareand software, shown for and within computer 101 may be utilized bysoftware deploying server 149 and/or a client computer 151 shown in FIG.1.

Exemplary computer 101 includes a processor 103 that is coupled to asystem bus 105. Processor 103 may utilize one or more processors, eachof which has one or more processor cores. A video adapter 107, whichdrives/supports a display 109 (which may be a touch-screen displaycapable of detecting touch inputs onto the display 109), is also coupledto system bus 105. System bus 105 is coupled via a bus bridge 111 to aninput/output (I/O) bus 113. An I/O interface 115 is coupled to I/O bus113. I/O interface 115 affords communication with various I/O devices,including a keyboard 117, a mouse 119, a media tray 121 (which mayinclude storage devices such as CD-ROM drives, multi-media interfaces,etc.), and external USB port(s) 125. While the format of the portsconnected to I/O interface 115 may be any known to those skilled in theart of computer architecture, in one embodiment some or all of theseports are universal serial bus (USB) ports.

As depicted, computer 101 is able to communicate with a softwaredeploying server 149 and/or other devices/systems using a networkinterface 129. Network interface 129 is a hardware network interface,such as a network interface card (NIC), etc. Network 127 may be anexternal network such as the Internet, or an internal network such as anEthernet or a virtual private network (VPN). In one or more embodiments,network 127 is a wireless network, such as a Wi-Fi network, a cellularnetwork, etc.

A hard drive interface 131 is also coupled to system bus 105. Hard driveinterface 131 interfaces with a hard drive 133. In one embodiment, harddrive 133 populates a system memory 135, which is also coupled to systembus 105. System memory is defined as a lowest level of volatile memoryin computer 101. This volatile memory includes additional higher levelsof volatile memory (not shown), including, but not limited to, cachememory, registers and buffers. Data that populates system memory 135includes computer 101's operating system (OS) 137 and applicationprograms 143.

OS 137 includes a shell 139, for providing transparent user access toresources such as application programs 143. Generally, shell 139 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 139 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 139, also called a command processor, is generally thehighest level of the operating system software hierarchy and serves as acommand interpreter. The shell provides a system prompt, interpretscommands entered by keyboard, mouse, or other user input media, andsends the interpreted command(s) to the appropriate lower levels of theoperating system (e.g., a kernel 141) for processing. While shell 139 isa text-based, line-oriented user interface, the present invention willequally well support other user interface modes, such as graphical,voice, gestural, etc.

As depicted, OS 137 also includes kernel 141, which includes lowerlevels of functionality for OS 137, including providing essentialservices required by other parts of OS 137 and application programs 143,including memory management, process and task management, diskmanagement, and mouse and keyboard management.

Application programs 143 include a renderer, shown in exemplary manneras a browser 145. Browser 145 includes program modules and instructionsenabling a world wide web (WWW) client (i.e., computer 101) to send andreceive network messages to the Internet using hypertext transferprotocol (HTTP) messaging, thus enabling communication with softwaredeploying server 149 and other systems.

Application programs 143 in computer 101's system memory (as well assoftware deploying server 149's system memory) also include a Logic forModifying a Computer-Based Interaction (LMCBI) 147. LMCBI 147 includescode for implementing the processes described below, including thosedescribed in FIGS. 2-4. In one embodiment, computer 101 is able todownload LMCBI 147 from software deploying server 149, including in anon-demand basis, wherein the code in LMCBI 147 is not downloaded untilneeded for execution. In one embodiment of the present invention,software deploying server 149 performs all of the functions associatedwith the present invention (including execution of LMCBI 147), thusfreeing computer 101 from having to use its own internal computingresources to execute LMCBI 147.

The hardware elements depicted in computer 101 are not intended to beexhaustive, but rather are representative to highlight essentialcomponents required by the present invention. For instance, computer 101may include alternate memory storage devices such as magnetic cassettes,digital versatile disks (DVDs), Bernoulli cartridges, and the like.These and other variations are intended to be within the spirit andscope of the present invention.

The present invention discloses a system and associated methods forperforming user segmentation using an unsupervised machine learningapproach, by evaluating eye gaze data, for targeted content delivery. Anexemplary embodiment of the present invention utilizes the followingsteps and methods.

When a user base population interacts with a computer or softwareprogram, their eye gaze data is collected to create a comprehensivecorpus of information.

The user base eye gaze corpus is processed by an unsupervised machinelearning algorithm to perform clustering of eye gaze data.

Once clustering is complete across the eye gaze corpus, eye gaze data,coupled with historical data on completed and failed targeted actions,are used to segment new and existing users, based on the clusters.

Once the user segmentation has been facilitated via eye gaze patterns,real-time interventions (modifications to content or format presented inthe software program user experience—UX) are made possible, in order tomaximize the likelihood of a user performing a target action.

The current invention utilizes the following technicalcomponents/assets/capabilities that are known in the prior art: astate-of-the-art mobile or web-based technology for capturing user eyegaze data, that does not require chin-strap calibration, such as the EyeTracking technology; and unsupervised machine learning algorithms toperform clustering.

With reference now to FIG. 2, an exemplary overview of one or moreembodiments of the present invention is presented. Assume that an eyegaze clustering engine 202 (i.e., part of LMCBI 147 shown in FIG. 1) hasaccess to database 204 of comprehensive users' eye gaze corpus. That is,database 204 is a collection of past eye gazes that users have performedwhen looking at certain areas on a user interface (UI). The eye gazeclustering engine 202 maps eye gaze data points from database 204 tofeature vectors, as shown in block 206. That is, in block 206 the eyegaze clustering engine 202 will map where past users were looking on aUI (eye gaze data points) to the content that was being displayed on theUI at the time of the gazes.

As shown in block 208, unsupervised clustering of the eye gaze datapoints is performed. That is, an algorithm is then run to clustertogether the eye gaze data points using a machine learning algorithm(that performs unsupervised clustering) to find hidden patterns and/orgroupings in the eye gaze data points.

This unsupervised clustering (segmentation) of the user base, using theeye gaze data, is performed when a user base interacts with a computeror software program. The eye gaze data is collected, using any knownstate of the art system, in order to collect eye gaze data used tocreate a comprehensive eye gaze corpus of information about the eyegazes.

Each entry of the eye gaze corpus is transformed to represent a uniquedata point (for instance, via a feature vector representation).

The user base eye gaze corpus is processed by a neural network employingan unsupervised machine learning algorithm to perform clustering. Anexemplary unsupervised machine learning algorithm is the K-means clusteralgorithm: Given a set of data points (or instances) of eye gaze data,denoted as {x1, x2, . . ., xn}, where xi=(xi1, xi2, . . . , xir) is avector in a real-valued space uniquely representing an eye gaze datapoint, and r is the number of attributes (dimensions) used to describethe unique eye gaze data point, the K-means algorithm partitions thecorpus of eye gaze data points into k clusters.

Once K-means clustering is complete across the eye gaze corpus,new/future user interactions with the computer or software program aremonitored using legacy analytics approaches to evaluate success andfailure scenarios for performing a target action. In the presentinvention, eye gaze data is also captured for segmenting (classifying)the user in real-time, based on the K-means clusters.

Using the correlation between successful user actions and eye gazepatterns, cluster labels are assigned to the K-means clusters in one ormore embodiments of the present invention. An example of this is asfollows: A group A of users exhibit an eye gaze pattern in which theirgaze settles on a “save 15% off now” image, then clicks the “buy”button. A group B of users exhibit an eye gaze pattern whereby theirgaze settles on “shipping costs” text, a “save 15% off now” image, thenclicks the “back” button. Group A could be labeled the “Probable toBuy”, and Group B could be labeled “Needs Online Chat Intervention”,such that this online chat intervention occurs at or before the point ofclicking the back button. This labeling of the clusters allows thesystem to select an appropriate cluster in order to generate anappropriate UI modification for the current user. That is, in thisexemplary embodiment, Group A is assigned a system generated label bythe system. Conceptually this generated label corresponds to “Probableto Buy”. Similarly, Group B is assigned a system generated label thatconceptually corresponds to “Needs Online Chat Intervention”. For usersassigned to the latter cluster, online chat intervention occurs at orbefore the point of clicking the back button.

In a preferred embodiment of the present invention, the usersegmentation is performed automatically—human intervention is onlyrequired to observe or interpret the higher-level meaning conveyed inthe resulting segmentation (i.e., to label the K-means clusters). Thatis, K-means clustering of a data set is unsupervised clustering, andwhile the K-means algorithm will group data points into clusters by ameasure of “similarity”, thus allowing the system to recognizesimilarities between the data points in a cluster.

Once the user segmentation has been facilitated via eye gaze patterns,real-time interventions or modifications to content or formatting of thecomputer or software program UX are made possible, to maximize thelikelihood of a target action being performed.

For example, and as shown in FIG. 2, assume that a user 210 (e.g., usingthe client computer 151 shown in FIG. 1), has her gaze data captured (bya gaze capturing device 224) as she is looking at the screen on herclient computer.

The gaze capturing device 224 is any gaze capturing device known in theprior art. For example, gaze capturing device 224 may point-map the faceof user 210 in order to determine the general direction in which theuser 210 is looking. The gaze capturing device 224 can then point-mapthe eyes of the user 210, in order to determine exactly where the user210 is looking on the display of the client computer 151.

The real-time user gaze data is depicted in FIG. 2 as block 212. Thisreal-time user eye gaze data (from block 212) is combined with a userprofile (block 214) of the user 210 by a user segmentation engine, whichmay also be part of LCMBI 147 shown in FIG. 1, as part of computer 101(i.e., a server) or as part of the client computer 151.

The user segmentation engine 216 uses the real-time user eye gaze data(block 212), the user profile (block 214), and a persisted record ofusers' actions (database 218) as inputs to a user segmentation algorithm(block 220).

As mentioned above, the real-time user eye gaze data (block 212)describes where the user 210 is currently looking at the UI on hercomputer.

The user profile (block 214) includes profile information about the user210 such as her web search history, her product purchasing history, hermedical profile (e.g., having “low vision”), her job title, etc.

The persisted record of users' actions (database 218) is a record ofother users looking at the same user interface as that which user 210 iscurrently looking (or alternatively user interfaces that are differentfrom what the user 210 is currently looking at) and their user actions.For example, database 218 may contain a record of other users who havelooked at a particular place on the same UI that the user 210 iscurrently looking at. The database 218 also includes a record of whatthese other users did after looking at the particular place on the UI(e.g., made a purchase from a website being displayed on the UI, leftthe website without making a purchase, asked for additional informationabout a product being displayed on the particular place on the UI,etc.). Thus, database 218 provides the user segmentation algorithm 220with information needed to generate a system output 222, which placesuser 210 into a particular segment of users who are known to havebenefitted (e.g., by better understanding content from a webpage, byhaving a better user experience—UX when engaging with the UI, byreceiving additional information about a product or information beingdisplayed on the UI, etc.) by a particular modification to the UI.

For example, and with reference now to FIG. 3, assume that the user 210has looked at pane 303 on a GUI 301 on her client computer 151 (see FIG.1). Based on the system output 222 generated in FIG. 2, the GUI 301 willbe automatically enhanced with a conversational interface 305 (e.g., achat window), that provides an intervention that offers the user theopportunity to receive real-time chat assistance, rather than leavingthe webpage being displayed on GUI 301.

With reference now to FIG. 4, a high-level flow chart of one or moreprocesses performed by one or more embodiments of the present inventionto modify a computer-based interaction is presented.

After initiator block 402, one or more processors (e.g., processor 103shown in FIG. 1) collect eye gaze data points to create an eye gazecorpus of information, as shown in block 404 and as described in FIG. 2.As described herein, the eye gaze data points describe an eye gaze ofviewers of a first set of at least one user interface (i.e., eye gazedata points of past viewers of the same UI, or alternatively another UI,as that being viewed by user 210, such as those eye gaze data pointsfound in database 204 in FIG. 2).

As described in block 406 in FIG. 4, the processor(s) apply anunsupervised machine learning algorithm to the eye gaze corpus togenerate a plurality of clusters based upon the eye gaze data points. Asdescribed herein, in one embodiment of the present invention theunsupervised machine learning algorithm is a k-means clusteringalgorithm.

As described in block 408, the processor(s) determine a target actionperformance of the first set of at least one user interface for each ofthe plurality of clusters (a particular action that the past userswanted to perform, such as purchasing a product, understanding textbetter, etc. when looking at the GUI/website).

As described in block 410, the processor(s) collect, from a devicehaving eye tracking technology (e.g., the client computer 151 being usedby the user 210 in FIG. 2), real time eye gaze data from a plurality ofcurrent users who are viewing a second set of at least one userinterface. That is, not only is user 210 looking at the user interface,but many other users are also looking at that same user interface. Allof the users (user 210 as well as the other users) are having their eyegaze tracked by a gaze tracking device, such as the gaze tracking device224 shown in FIG. 2.

As described in block 412, the processor(s) segment the plurality ofcurrent users (user 210 as well as the other users) based upon theplurality of clusters by analyzing patterns among the real-time eye gazedata and by analyzing scenario data pertaining to the target actionperformance. That is, the user segmentation algorithm 220 shown in FIG.2 will create segments (partitions) of users into clusters, based on 1)where they are looking (the real-time eye gaze data), and 2) what targetaction is desired (scenario data pertaining to the target actionperformance).

For example, and in one or more embodiments of the present invention,the scenario data pertaining to target action performance includeshistorical data on failed or successful scenarios with respect tocompletion of at least one target action. That is, if a computer-basedinteraction (e.g., a client's user experience with a user interface),resulted in a successful scenario in which the user was directed to theappropriate product, received additional clarification about contentshown in the user interface, etc., then that user interface is deemed tobe a good fit for the current user (assuming that the current user'sgaze pattern and profile matches those of other users of that userinterface). However, if the computer-based interaction resulted in afailure scenario in which the user was not directed to the appropriateproduct, did not receive additional clarification about content shown inthe user interface, etc., then that user interface is deemed to be apoor fit for the current user, and is not used to modify the currentuser interface for that user.

As described in block 414, responsive to the segmenting of the pluralityof users, the processor(s) modify a computer-based interaction for eachsegment of the at least one of the plurality of users in order tomaximize target action performance of the second set of at least oneuser interface.

The present invention provides various embodiments for modifying thecomputer-based interaction. For example, and in one embodiment,modifying the computer-based interaction with at least one of theplurality of users includes modifying at least one aspect of a graphicaluser interface of a software program, such as displaying promotionalcontent (i.e., an advertisement or special offer for a product that isbeing viewed on the user interface), displaying additional clarificationcontent related to the software program (i.e., content displayed on thegraphical user interface), etc.

The flow chart shown in FIG. 4 ends at terminator block 416.

In an embodiment of the present invention, the processor(s) categorizethe plurality of clusters based upon a predefined significance of one ormore aspects of the segmentation. For example, assume in block 208 inFIG. 2 that the eye gaze clustering engine 202 has generated multipleclusters of eye gaze data points. However, assume further that a firstset of clusters was generated based on aspect A (e.g., based on onlywhere the users were looking), while a second set of clusters wasgenerated based on aspect B (e.g., where the users were looking as wellas their profiles). If the gaze locations are the most significantaspect used in the segmentation, then the first set of clusters are moresignificant than the second set of clusters, and thus are given a higherpriority for use by the user segmentation engine 216 shown in FIG. 2.

In an embodiment of the present invention, the eye gaze of viewers ofthe first set of at least one user interface and the real-time eye gazedata from the plurality of current users who are viewing the second setof at least one user interface are based on a pathological nystagmus ofthe viewers of the first set of at least one user interface and theplurality of current users who are viewing the second set of at leastone user interface. That is, the segmentation is based not just on wherethe users are looking, but how they look. More specifically, if theusers have a same vision issue (e.g., nystagmus), then the system willenhance/modify the user interface not only based on where the users arelooking (e.g., by adding additional content, explanations, offers,etc.), but will also enhance/modify the user interface to modify theappearance of the user interface (e.g., by automatically enlargingfonts, changing hues, etc.).

Consider now the following exemplary use case in e-Commerce for thepresent invention.

Assume that CompanyX is an online market place of products and services.As such, CompanyX may (or does) employ eye gaze tracking of its userbase for one year, in order to cultivate a comprehensive corpus of eyegaze data (see database 204 in FIG. 2).

CompanyX then translates the eye gaze corpus into a set of data points,corresponding to feature vectors (see block 206 in FIG. 2), and utilizesK-means clustering to group similar eye gaze patterns (see block 208 inFIG. 2).

CompanyX then rolls out a limited release of new product called WidgetA.CompanyX employs eye gaze tracking of users viewing WidgetA (e.g., user210 as well as other current real-time users), while also recordingsuccessful and abandoned purchases over a one month period.

Using the similarity of user eye gaze patterns to the K-means clusters,which are also correlated with the history of successful and failedpurchases over the limited release period, CompanyX labels one K-meanscluster as “most likely to buy”, and another “most likely to abort”.

CompanyX then rolls out WidgetA to all markets world-wide. When a newuser views WidgetA, CompanyX employs eye gaze tracking to categorize(segment) the user as “requires assistance” because the user's eye gazepatterns are most similar to the “most likely to abort” K-means cluster.

Real-time intervention, in the form of chat assistance or displaying aspecial promotion, is performed, and the user completes the purchase ofWidgetA.

Thus, as described in one or more embodiments herein, the presentinvention presents a system and associated methods where a user base,interacting with a computer or software program, can be automaticallysegmented based on eye gaze patterns. Once user segmentation isperformed, real-time interventions or modifications to content orformatting of the computer or software program UX can be performed, inorder to maximize the likelihood that a user will perform a targetaction, thus providing a technological improvement over the prior artby 1) more efficiently using computer resources when generating UX(e.g., UIs), and 2) optimizing the UX for future users.

The “gaze pattern” utilized in various embodiments of the presentinvention is based on content, appearance, position, and/or acombination of any or all of these factors, as well as (in oneembodiment) a physical condition of the user (e.g., having a visioncondition such as nystagmus, in which the eyes rapidly shift from sideto side). That is, in one embodiment a user is profiled based on thetype of content that the user is shown to favor based on terms that theuser likes (e.g., “50% off!”), the physical appearance of the content(e.g., having a flashing red background), and/or or where the content isin the display (e.g., the right upper quadrant). In one embodiment, thephysical condition of the user's eyes (e.g., having a medical conditionsuch as nystagmus, having eyes that are drooping, thus indicating a lackof sleep, etc.) also reflect the user's “gaze pattern”.

The present invention may be implemented in one or more embodimentsusing cloud computing. Nonetheless, it is understood in advance thatalthough this disclosure includes a detailed description on cloudcomputing, implementation of the teachings recited herein is not limitedto a cloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-54Nshown in FIG. 5 are intended to be illustrative only and that computingnodes 10 and cloud computing environment 50 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and computer-based interaction modificationprocessing 96, which performs one or more of the features of the presentinvention described herein.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentinvention. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of various embodiments of the present invention has beenpresented for purposes of illustration and description, but is notintended to be exhaustive or limited to the present invention in theform disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the present invention. The embodiment was chosen and describedin order to best explain the principles of the present invention and thepractical application, and to enable others of ordinary skill in the artto understand the present invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

Any methods described in the present disclosure may be implementedthrough the use of a VHDL (VHSIC Hardware Description Language) programand a VHDL chip. VHDL is an exemplary design-entry language for FieldProgrammable Gate Arrays (FPGAs), Application Specific IntegratedCircuits (ASICs), and other similar electronic devices. Thus, anysoftware-implemented method described herein may be emulated by ahardware-based VHDL program, which is then applied to a VHDL chip, suchas a FPGA.

Having thus described embodiments of the present invention of thepresent application in detail and by reference to illustrativeembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of the presentinvention defined in the appended claims.

What is claimed is:
 1. A method comprising: collecting, by one or moreprocessors, eye gaze data points to create an eye gaze corpus ofinformation, wherein the eye gaze data points describe an eye gaze ofviewers of a first set of at least one user interface; determining, byone or more processors, one or more particular locations on the firstset of at least one user interface that the viewers were looking atbased on the eye gaze data points; applying, by one or more processors,an unsupervised machine learning algorithm to the eye gaze corpus togenerate a plurality of clusters based upon the eye gaze data points,wherein each cluster comprises a set of viewers that were looking at asame particular location on the first set of at least one userinterface; determining, by one or more processors, a target actionperformance of the first set of at least one user interface for each ofthe plurality of clusters, wherein a target action is an action that apresenter of the first set of at least one user interface desires theviewers to perform; collecting, by one or more processors and from adevice having eye tracking technology, real time eye gaze data from aplurality of current users who are viewing a second set of at least oneuser interface, wherein the real time eye gaze data describe one or moreparticular locations on the second set of at least one user interfacethat the current users are looking at; segmenting, by one or moreprocessors, the plurality of current users based upon the plurality ofclusters by analyzing patterns among the real-time eye gaze data and byanalyzing scenario data pertaining to the target action performance; andresponsive to the segmenting of the plurality of current users,modifying, by one or more processors, a computer-based interaction forat least one segment of the plurality of current users in order tomaximize target action performance of the second set of at least oneuser interface.
 2. The method of claim 1, wherein the unsupervisedmachine learning algorithm is a k-means clustering algorithm that isprocessed by a neural network.
 3. The method of claim 1, wherein thescenario data pertaining to the target action performance includeshistorical data on failed scenarios with respect to completion of atleast one target action.
 4. The method of claim 1, wherein the scenariodata pertaining to the target action performance includes historicaldata on successful scenarios with respect to completion of at least onetarget action.
 5. The method of claim 1, wherein the scenario dataincludes historical data that is based upon profile data associated withat least one of the plurality of current users, and wherein the profiledata describes a product purchasing history of at least one of theplurality of current users.
 6. The method of claim 1, furthercomprising: categorizing, by one or more processors, the plurality ofclusters based upon a predefined significance of one or more aspects ofthe segmenting.
 7. The method of claim 1, wherein modifying thecomputer-based interaction with at least one segment of the plurality ofcurrent users comprises modifying at least one aspect of a graphicaluser interface of a software program.
 8. The method of claim 7, whereinmodifying at least one aspect of the graphical user interface comprisesdisplaying an online chat intervention on the graphical user interfacein response to a current user clicking a back button to leave thegraphical user interface.
 9. The method of claim 7, wherein modifying atleast one aspect of the graphical user interface comprises displayingadditional clarification content related to the software program via thegraphical user interface.
 10. The method of claim 1, wherein modifyingthe computer-based interaction with at least one segment of theplurality of current users comprises presenting a conversationalinterface on a graphical user interface.
 11. The method of claim 1,wherein the first set of at least one user interface and the second setof at least one user interface are a same set of at least one userinterface.
 12. The method of claim 1, wherein the first set of at leastone user interface and the second set of at least one user interface aredifferent sets of at least one user interface.
 13. The method of claim1, wherein the eye gaze of the viewers of the first set of at least oneuser interface and the real-time eye gaze data from the plurality ofcurrent users who are viewing the second set of at least one userinterface are based on a pathological nystagmus of the viewers of thefirst set of at least one user interface and the plurality of currentusers who are viewing the second set of at least one user interface. 14.The method of claim 1, wherein the eye gaze of the viewers of the firstset of at least one user interface and the real-time eye gaze data fromthe plurality of current users who are viewing the second set of atleast one user interface are based on a position on a graphical userinterface at which the viewers of the first set of at least one userinterface and the plurality of current users who are viewing the secondset of at least one user interface are visually focused.
 15. A computerprogram product for modifying a computer-based interaction based onhistorical eye gaze data, the computer program product comprising anon-transitory computer readable storage device having programinstructions embodied therewith, the program instructions readable andexecutable by a computer to perform a method comprising: collecting eyegaze data points to create an eye gaze corpus of information, whereinthe eye gaze data points describe an eye gaze of viewers of a first setof at least one user interface; determining one or more particularlocations on the first set of at least one user interface that theviewers were looking at based on the eye gaze data points; applying anunsupervised machine learning algorithm to the eye gaze corpus togenerate a plurality of clusters based upon the eye gaze data points,wherein each cluster comprises a set of viewers that were looking at asame particular location on the first set of at least one userinterface; determining a target action performance of the first set ofat least one user interface for each of the plurality of clusters;collecting, from a device having eye tracking technology, real time eyegaze data from a plurality of current users who are viewing a second setof at least one user interface, wherein the real time eye gaze datadescribe one or more particular locations on the second set of at leastone user interface that the current users are looking at; segmenting theplurality of current users based upon the plurality of clusters byanalyzing patterns among the real-time eye gaze data and by analyzingscenario data pertaining to the target action performance; andresponsive to the segmenting of the plurality of current users,modifying a computer-based interaction for at least one segment of theplurality of current users in order to maximize target actionperformance of the second set of at least one user interface.
 16. Thecomputer program product of claim 15, wherein modifying thecomputer-based interaction with at least one of the plurality of currentusers comprises presenting a conversational interface on a graphicaluser interface.
 17. The computer program product of claim 15, whereinthe program instructions are provided as a service in a cloudenvironment.
 18. A computer system comprising one or more processors,one or more computer readable memories, one or more computer readablestorage mediums, and program instructions stored on at least one of theone or more storage mediums for execution by at least one of the one ormore processors via at least one of the one or more memories, the storedprogram instructions comprising: program instructions to collect eyegaze data points to create an eye gaze corpus of information, whereinthe eye gaze data points describe an eye gaze of viewers of a first setof at least one user interface; program instructions to determine one ormore particular locations on the first set of at least one userinterface that the viewers were looking at based on the eye gaze datapoints; program instructions to apply an unsupervised machine learningalgorithm to the eye gaze corpus to generate a plurality of clustersbased upon the eye gaze data points, wherein each cluster comprises aset of viewers that were looking at a same particular location on thefirst set of at least one user interface; program instructions todetermine a target action performance of the first set of at least oneuser interface for each of the plurality of clusters; programinstructions to collect, from a device having eye tracking technology,real time eye gaze data from a plurality of current users who areviewing a second set of at least one user interface, wherein the realtime eye gaze data describe one or more particular locations on thesecond set of at least one user interface that the current users arelooking at; program instructions to segment the plurality of currentusers based upon the plurality of clusters by analyzing patterns amongthe real-time eye gaze data and by analyzing scenario data pertaining tothe target action performance; and program instructions to, responsiveto segmenting the plurality of current users, modify a computer-basedinteraction for at least one segment of the plurality of current usersin order to maximize target action performance of the second set of atleast one user interface.
 19. The method of claim 1, wherein thescenario data pertaining to the target action performance describes theaction that the presenter of the first set of at least one userinterface desires the viewers to perform.
 20. The computer system ofclaim 18, wherein the program instructions are provided as a service ina cloud environment.